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This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing and machine learning, such as resiliency to stragglers and bandwidth bottleneck. Furthermore, coded computing can enable (information-theoretically) secure and private learning over untrusted…

The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy tradeoff in dynamic decision-making problems. The central question is: how can a decision maker take actions that are efficient for…

Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups, including house-hunting honeybees and schooling fish, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. Bio: Prof. Naomi…

Resilience of a transportation system is its ability to operate under adverse events like incidents and storms. Availability of real-time traffic data provides new opportunities for predicting travelers’ routing behavior and implementing network control operations during adverse events. In this talk, we will discuss two problems: controlling highway corridors in response to disruptions and modeling…

Electric power: done wrong, it drags the economy and environment down; done right, it could help to create a more efficient, brighter, and cleaner future. Better policy, planning, and operations models--both simple analytical, and complex computational ones--are essential if we're going to do it right. Better modeling is also fun, as the math of electricity…

Computation in the brain has been modeled productively at many scales, ranging from molecules to dendrites, neurons, and synapses, all the way to the whole brain models useful in cognitive science. I will discuss recent work on an intermediate layer, involving assemblies of neurons --- that is to say, sets of neurons firing together in…

Modern data sets are often distributed across multiple machines and processors, and bandwidth and energy limitations in networks and within multiprocessor systems often impose significant bottlenecks on the performance of algorithms. Motivated by this trend, we consider the problem of estimating high-dimensional distributions and parameters in a distributed network, where each node in the network…

Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen, however, as incompatible with taking advantage of a block-separable structure. In fact, when articulated in the right way, they can produce decomposition algorithms in which low-dimensional subproblems can be solved…

We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs, where a trained neural network (called a critic) determines whether a given sample is…